import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import numpy as np 
from Losses.Mask_bce_loss import Attention_loss

class SpatialAttention(nn.Module):
    def __init__(self, channel_in, channel_size):
        super(SpatialAttention, self).__init__()
        
        self.conv1 = nn.Conv2d(channel_in, channel_size, kernel_size=3, padding=1)
        self.relu1 = nn.ReLU()

        self.conv2 = nn.Conv2d(channel_size, channel_size, kernel_size=3, padding=1)
        self.relu2 = nn.ReLU()

        self.conv3 = nn.Conv2d(channel_size, 1, kernel_size=3, padding=1)

        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        out = self.conv1(x)
        out = self.relu1(out)

        out = self.conv2(out)
        out = self.relu2(out)

        out = self.conv3(out)

        out = self.sigmoid(out)

        return out

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)


        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, inputs):

        img = inputs

        x = self.conv1(img)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        
        return x


        
class Resnet34_attention(nn.Module):

    def __init__(self):
        super(Resnet34_attention, self).__init__()
        self.model = ResNet(BasicBlock, [3, 4, 6])

        self.sa1 = SpatialAttention(256, 512)
        self.aloss = Attention_loss()

    def forward(self, inputs):
        img, mask = inputs
        x = self.model(img)
        x = self.sa1(x)
        x = self.aloss(x, mask)
        return x


if __name__ == '__main__': 
    model = Resnet34_attention()

    for name, m in model.named_modules():
        print(name)
    print('*'*30)

    model_dict = model.state_dict()
    print(model_dict.keys())



    # checkpoint = torch.load('Model_training_checkpoints/model_resnet34_cheahom_triplet_epoch_17_roc0.9339.pt')
    # pretrained_dict = checkpoint['model_state_dict']
    # model_dict=model.state_dict()

    # pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
    # model_dict.update(pretrained_dict)
    # model.load_state_dict(model_dict)
    # print('okokokokok')



